| Date
|
Name
|
Paper number
|
Title
|
Link to the paper
|
Link to the summary
|
| Feb 15 (example) |
Ri Wang |
|
Sequence to sequence learning with neural networks. |
Paper |
[Summary]
|
| Oct 25 |
Dhruv Kumar |
1 |
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs |
Paper |
Summary
|
| Oct 25 |
Amirpasha Ghabussi |
2 |
DCN+: Mixed Objective And Deep Residual Coattention for Question Answering |
Paper |
Summary
|
| Oct 25 |
Juan Carrillo |
3 |
Hierarchical Representations for Efficient Architecture Search |
Paper |
Summary
|
| Oct 30 |
Manpreet Singh Minhas |
1 |
End-to-end Active Object Tracking via Reinforcement Learning |
Paper |
Summary
|
| Oct 30 |
Marvin Pafla |
2 |
Fairness Without Demographics in Repeated Loss Minimization |
Paper |
Summary
|
| Oct 30 |
Glen Chalatov |
3 |
Pixels to Graphs by Associative Embedding |
Paper |
Summary
|
| Nov 1 |
Sriram Ganapathi Subramanian |
1 |
Differentiable plasticity: training plastic neural networks with backpropagation |
Paper |
Summary
|
| Nov 1 |
Hadi Nekoei |
1 |
Synthesizing Programs for Images using Reinforced Adversarial Learning |
Paper |
Summary
|
| Nov 1 |
Henry Chen |
1 |
DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks |
Paper |
|
| Nov 6 |
Nargess Heydari |
2 |
Wavelet Pooling For Convolutional Neural Networks Networks |
Paper |
|
| Nov 6 |
Aravind Ravi |
3 |
Towards Image Understanding from Deep Compression Without Decoding |
Paper |
|
| Nov 6 |
Ronald Feng |
1 |
Learning to Teach |
Paper |
|
| Nov 8 |
Neel Bhatt |
1 |
Annotating Object Instances with a Polygon-RNN |
Paper |
|
| Nov 8 |
Jacob Manuel |
2 |
|
|
|
| Nov 8 |
Charupriya Sharma |
2 |
|
|
|
| NOv 13 |
Sagar Rajendran |
1 |
Zero-Shot Visual Imitation |
Paper |
|
| Nov 13 |
Jiazhen Chen |
2 |
|
|
|
| Nov 13 |
Neil Budnarain |
2 |
PixelNN: Example-Based Image Synthesis |
Paper |
|
| NOv 15 |
Zheng Ma |
3 |
Reinforcement Learning of Theorem Proving |
Paper |
|
| Nov 15 |
Abdul Khader Naik |
4 |
|
|
|
| Nov 15 |
Johra Muhammad Moosa |
2 |
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin |
Paper |
|
| NOv 20 |
Zahra Rezapour Siahgourabi |
1 |
|
|
|
| Nov 20 |
Shubham Koundinya |
6 |
|
|
|
| Nov 20 |
Salman Khan |
|
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples |
paper |
|
| NOv 22 |
Soroush Ameli |
3 |
Learning to Navigate in Cities Without a Map |
paper |
|
| Nov 22 |
Ivan Li |
23 |
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate |
Paper |
|
| Nov 22 |
Sigeng Chen |
2 |
|
|
|
| Nov 27 |
Aileen Li |
8 |
Spatially Transformed Adversarial Examples |
Paper |
|
| NOv 27 |
Xudong Peng |
9 |
Multi-Scale Dense Networks for Resource Efficient Image Classification |
Paper |
|
| Nov 27 |
Xinyue Zhang |
10 |
An Inference-Based Policy Gradient Method for Learning Options |
Paper |
|
| NOv 29 |
Junyi Zhang |
11 |
Autoregressive Convolutional Neural Networks for Asynchronous Time Series |
Paper |
|
| Nov 29 |
Travis Bender |
12 |
Automatic Goal Generation for Reinforcement Learning Agents |
Paper |
|
| Nov 29 |
Patrick Li |
12 |
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices |
Paper |
|
| Makup |
Ruijie Zhang |
1 |
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction |
Paper |
|
| Makup |
Ahmed Afify |
2 |
Don't Decay the Learning Rate, Increase the Batch Size |
Paper |
|
| Makup |
Gaurav Sahu |
3 |
TBD |
|
|
| Makup |
Kashif Khan |
4 |
Wasserstein Auto-Encoders |
Paper |
|
| Makup |
Shala Chen |
|
A NEURAL REPRESENTATION OF SKETCH DRAWINGS |
|
|
| Makup |
Ki Beom Lee |
|
|
|
|
| Makup |
Wesley Fisher |
|
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling |
Paper |
Summary
|